International Journal of Information Technology and Computer Science(IJITCS)
ISSN: 2074-9007 (Print), ISSN: 2074-9015 (Online)
Published By: MECS Press
IJITCS Vol.8, No.3, Mar. 2016
Customer Credit Risk Assessment using Artificial Neural Networks
Full Text (PDF, 360KB), PP.58-66
Since the granting of banking facilities in recent years has faced problems such as customer credit risk and affects the profitability directly, customer credit risk assessment has become imperative for banks and it is used to distinguish good applicants from those who will probably default on repayments. In credit risk assessment, a score is assigned to each customer then by comparing it with the cut-off point score which distinguishes two classes of the applicants, customers are classified into two credit statuses either a good or bad applicant. Regarding good performance and their ability of classification, generalization and learning patterns, Multi-layer Perceptron Neural Network model trained using various Back-Propagation (BP) algorithms considered in designing an evaluation model in this study. The BP algorithms, Levenberg-Marquardt (LM), Gradient descent, Conjugate gradient, Resilient, BFGS Quasi-newton, and One-step secant were utilized. Each of these six networks runs and trains for different numbers of neurons within their hidden layer. Mean squared error (MSE) is used as a criterion to specify optimum number of neurons in the hidden layer. The results showed that LM algorithm converges faster to the network and achieves better performance than the other algorithms. At last, by comparing classification performance of neural network with a number of classification algorithms such as Logistic Regression and Decision Tree, the neural network model outperformed the others in customer credit risk assessment. In credit models, because the cost that Type II error rate imposes to the model is too high, therefore, Receiver Operating Characteristic curve is used to find appropriate cut-off point for a model that in addition to high Accuracy, has lower Type II error rate.
Cite This Paper
Nasser Mohammadi, Maryam Zangeneh,"Customer Credit Risk Assessment using Artificial Neural Networks", International Journal of Information Technology and Computer Science(IJITCS), Vol.8, No.3, pp.58-66, 2016. DOI: 10.5815/ijitcs.2016.03.07
C. S. Ong, J. J. Huang, G.H. Tzeng, “Building credit scoring models using genetic programming”, Expert Syst Appl, vol. 29, pp. 41–47, 2005.
L. Nanni, A. Lumini, “An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring”, Expert Syst Appl, vol. 36, pp. 1-4, 2009.
A. I Marqués, V. García, J. S. Sánchez, “Exploring the Behavior of Base Classifiers in Credit Scoring Ensembles” Expert Syst Appl, vol. 39, pp. 10244–10250, 2012.
S. Haykin, “Neural Networks: A Comprehensive Foundation”, 2nd ed. NJ, USA: Prentice Hall, 1999.
R. Battit, “First and Second Order Methods of Learning: Between the Steepest Descent and Newton’s Method”, Neural Network, pp. 4141–4166, 1991.
M. T Hagan, M. B. Menhaj, “Training Feed Forward Networks with the Marquardt Algorithm”, IEEE Trans Neural Netw, vol. 5, pp. 989–993, 1994.
L. Yu, S. Wang, K. K. Lai, “An Intelligent-Agent-Based Fuzzy Group Decision Making Model for Financial Multicriteria Decision Support: The Case of Credit Scoring”, Eur J Oper Res, vol. 195, pp. 942-952, 2009.
N. C. Hsieh, L. P. Hung, “A data driven ensemble classifier for credit scoring analysis”, Expert Syst Appl, vol. 37, pp. 534 – 545, 2010.
C. L. Chuang, S. T. Huang, “A hybrid neural network approach for credit scoring”, Expert Syst Appl, vol. 28, pp. 185-196, 2011.
S. Akkoc, “An empirical comparison of conventional techniques, neural networks and the three stage hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) model for credit scoring analysis: The case of Turkish credit card data”, Eur J Oper Res, vol. 222, pp. 168-178, 2012.
J. Kruppa, A. Schwarz, G. Arminger, and A. Ziegler, “Consumer credit risk: Individual probability estimates using machine learning”, Expert Syst Appl, vol. 40, pp. 5125-5131, 2013.
M. Chen, Z. Yao, “Classification techniques of neural networks using improved genetic algorithm”, In: IEEE 2008 Genetic and Evolutionary Computing; Hubei, China: IEEE. pp. 115-119, 25-26 Sept. 2008.
M. Shahidehpour, H. Yamin, Z. Li, “Market Operations in Electric Power Systems: Forecasting, Scheduling, and Risk Management”, New York, USA: Wiley-IEEE Press, 2002.
J. R. Quinlan, “Discovering rules by induction from large collections of examples”, In: D. Michie, editor. Expert Systems in the Micro Electronic Age. Edinburgh: Edinburgh University Press, pp. 168-201, 1979.
J. R. Quinlan, “Learning efficient classification procedures and their application to chess endgames”, in: R.S. Michalski and J.G. Carbonnell and T.M. Mitchell. Machine learning: An artificial intelligence approach. Tioga, pp. 463-482, 1983.
M. S. Mirtalaei, M. Saberi, O. K. Hussain, B. Ashjari, F.K. Hussain, “A trust-based bio-inspired approach for credit lending decisions”, COMPUTING, vol. 94, pp. 541 – 577, 2012.
A. Khashman, “Credit risk evaluation using neural networks: Emotional versus conventional models”, Appl Soft Comput, vol. 11, pp. 5477-5484, 2011.
S. Y. Chang, T.Y Yeh, “An artificial immune classifier for credit scoring analysis”, Appl. Soft Comput, vol. 12, pp. 611-618, 2012.
A. Blanco, R. Pino-Mejías, J. Lara, S. Rayo, “Credit scoring models for the microfinance industry using neural networks: Evidence from Peru”, Expert Syst Appl. Vol. 40, pp. 356–364, 2012.
L. J. Kao, C. C. Chiu, F. Y. Chiu, “A Bayesian latent variable model with classification and regression tree approach for behavior and credit scoring”, Knowl Based Syst, vol. 36, p. 245–252, 2012.
D. West, “Neural network credit scoring models”, Comput Oper Res, vol. 27, pp. 1131–1152, 2000.
B. Baesens, T. Van Gestel, S. Viaene, M. Stepanova, J. Suykens, J. Vanthienen, “Benchmarking state of the art classification algorithms for credit scoring”, J Oper Res Soc, vol. 54, pp. 627–635, 2003.
E. Cholongitas, M. Senzolo, D. Patch, S. Shaw, C. Hui, A. K. Burroughs, “Review article: scoring systems for assessing prognosis in critically ill adult cirrhotics”, Aliment Pharmacol, vol. 24: 453–464.